Application of long-read sequencing to elucidate complex pharmacogenomic regions: a proof of principle.

The use of pharmacogenomics in clinical practice is becoming standard of care. However, due to the complex genetic makeup of pharmacogenes, not all genetic variation is currently accounted for. Here, we show the utility of long-read sequencing to resolve complex pharmacogenes by analyzing a well-characterised sample. This data consists of long reads that were processed to resolve phased haploblocks. 73% of pharmacogenes were fully covered in one phased haploblock, including 9/15 genes that are 100% complex. Variant calling accuracy in the pharmacogenes was high, with 99.8% recall and 100% precision for SNVs and 98.7% precision and 98.0% recall for Indels. For the majority of gene-drug interactions in the DPWG and CPIC guidelines, the associated genes could be fully resolved (62% and 63% respectively). Together, these findings suggest that long-read sequencing data offers promising opportunities in elucidating complex pharmacogenes and haplotype phasing while maintaining accurate variant calling.

Development of an extensive workflow for comprehensive clinical pharmacogenomic profiling: lessons from a pilot study on 100 whole exome sequencing data.

This pilot study is aimed at implementing an approach for comprehensive clinical pharmacogenomics (PGx) profiling. Fifty patients with cardiovascular diseases and 50 healthy individuals underwent whole-exome sequencing. Data on 1800 PGx genes were extracted and analyzed through deep filtration separately. Theoretical drug induced phenoconversion was assessed for the patients, using sequence2script. In total, 4539 rare variants (including 115 damaging non-synonymous) were identified. Four publicly available PGx bioinformatics algorithms to assign PGx haplotypes were applied to nine selected very important pharmacogenes (VIP) and revealed a 45-70% concordance rate. To ensure availability of the results at point-of-care, actionable variants were stored in a web-hosted database and PGx-cards were developed for quick access and handed to the study subjects. While a comprehensive clinical PGx profile could be successfully extracted from WES data, available tools to interpret these data demonstrated inconsistencies that complicate clinical application.

Patterns of topical corticosteroids prescriptions in children with asthma.

OBJECTIVES: To study topical corticosteroid use in Dutch asthmatic children using pharmacy dispensing data and to assess whether Dutch physicians prescribe topical corticosteroids in this population according to clinical guidelines. METHODS: Medication histories of children using asthma medication were extracted from the pharmacy dispensing system in 100 Dutch community pharmacies. The incidence rate and the potency of topical corticosteroid prescriptions per age were assessed. The topical corticosteroid incidence rates of the different age groups were compared using the Pearson chi-square test. Generalized linear models were used to study the prescription behavior of general practitioners and atopic dermatitis-related specialists regarding different classes of topical corticosteroids. RESULTS: Thirty percent of the infants received a topical corticosteroid prescription, compared with 15%-18% of the children aged 4 and older. Similarly, the mean number of topical corticosteroid prescriptions in infants was 2.2 per year, compared with 1.6-1.9 in children aged 4 and older. In concordance with the clinical guidelines, we observed that atopic dermatitis-related specialists more often prescribed first prescriptions of potent and very potent topical corticosteroids than general practitioners (relative risk = 2.55, 95% confidence interval = 1.79-3.63). Statistically significant differences (P < .01) were found between potencies of prescribed topical corticosteroids. CONCLUSION: Younger children receive more topical corticosteroid prescriptions than children aged 4 and older, and there is a statistically significantly higher prescription rate of topical corticosteroid for infants. Sometimes general practitioners do not follow guidelines and prescribe more-potent topical corticosteroids without a prior prescription of the same potency by a specialist.

The effect of genetic variants in the transcription factor TSPYL family on the CYP3A4 mediated cyclosporine metabolism in kidney transplant patients.

CYP3A4 activity shows considerable interindividual variability. Although studies indicate 60%-80% is heritable, common single nucleotide variants (SNVs) in CYP3A4 together only explain ~10%. Transcriptional factors, such as the testis-specific Y-encoded-like proteins (TSPYLs) family, have been reported to regulate the expression of CYP enzymes including CYP3A4 in vitro. Here, we investigated the effect of genetic variants in TSPYL on CYP3A4 activity using data from a clinical study and a human liver bank. Five SNVs (rs3828743, rs10223646, rs6909133, rs1204807, and rs1204811) in TSPYL were selected because of a reported effect on CYP3A4 expression in vitro or suggested clinical effect. For the clinical study, whole blood concentrations, clinical data, and DNA were available from 295 kidney transplant recipients participating in the prospective MECANO study. A multivariate pharmacokinetic model adjusted for body weight, steroid treatment, and CYP3A4 genotype was used to assess the effect of the genetic variants on cyclosporine clearance. In multivariate analysis, homozygous carriers of rs3828743 had a 18% lower cyclosporin clearance compared to the wild-type and heterozygous patients (28.72 vs. 35.03 L/h, p = 0.018) indicating a lower CYP3A4 activity and an opposite direction of effect compared to the previously reported increased CYP3A4 expression. To validate, we tested associations between rs3828743 and CYP3A4 mRNA and protein expression as well as enzyme activity with data from a liver bank (n = 150). No association with any of these end points was observed. In conclusion, the totality of evidence is not in support of a significant role for TSPYL SNV rs3828743 in explaining variability in CYP3A4 activity.

Artificial intelligence in pharmacology research and practice.

In recent years, the use of artificial intelligence (AI) in health care has risen steadily, including a wide range of applications in the field of pharmacology. AI is now used throughout the entire continuum of pharmacology research and clinical practice and from early drug discovery to real-world datamining. The types of AI models used range from unsupervised clustering of drugs or patients aimed at identifying potential drug compounds or suitable patient populations, to supervised machine learning approaches to improve therapeutic drug monitoring. Additionally, natural language processing is increasingly used to mine electronic health records to obtain real-world data. In this mini-review, we discuss the basics of AI followed by an outline of its application in pharmacology research and clinical practice.

From gene to dose: Long-read sequencing and *-allele tools to refine phenotype predictions of CYP2C19.

Background: Inter-individual differences in drug response based on genetic variations can lead to drug toxicity and treatment inefficacy. A large part of this variability is caused by genetic variants in pharmacogenes. Unfortunately, the Single Nucleotide Variant arrays currently used in clinical pharmacogenomic (PGx) testing are unable to detect all genetic variability in these genes. Long-read sequencing, on the other hand, has been shown to be able to resolve complex (pharmaco) genes. In this study we aimed to assess the value of long-read sequencing for research and clinical PGx focusing on the important and highly polymorphic CYP2C19 gene. Methods and Results: With a capture-based long-read sequencing panel we were able to characterize the entire region and assign variants to their allele of origin (phasing), resulting in the identification of 813 unique variants in 37 samples. To assess the clinical utility of this data we have compared the performance of three different *-allele tools (Aldy, PharmCat and PharmaKU) which are specifically designed to assign haplotypes to pharmacogenes based on all input variants. Conclusion: We conclude that long-read sequencing can improve our ability to characterize the CYP2C19 locus, help to identify novel haplotypes and that *-allele tools are a useful asset in phenotype prediction. Ultimately, this approach could help to better predict an individual's drug response and improve therapy outcomes. However, the added value in clinical PGx might currently be limited.

Study protocol of the HD-MED study aiming to personalize drug treatment in Huntington's disease: a longitudinal, observational study to assess medication use and efficacy in relation to pharmacogenetics.

Background: Huntington's disease (HD) is a hereditary, neurodegenerative disorder characterized by motor, cognitive, and psychiatric symptoms. Currently, HD can only be managed symptomatically, including a large variety of prescribed drugs. Many HD patients experience negative medication effects (e.g. side effects or non-response). Pharmacogenetic (PGx) studies show how genetic variation affects both medication efficacy and toxicity and holds the potential to improve these outcomes of drug treatment. Primary objective: To classify the effect of the PGx profile of CYP2C19 and CYP2D6 in HD gene expansion carriers on negative medication effects of HD-related medication. Design: Multicenter, observational study with 1-year follow-up. Adult HD gene expansion carriers who use one or more HD-related medications are eligible to participate. Methods and analysis: A detailed overview of medication use, medication efficacy, and side effects is retrospectively and prospectively collected via medication diaries, questionnaires, phone calls, and pharmacy medication verification schemes. PGx analysis on whole blood-extracted DNA is performed with Agena Bioscience VeriDose® Core Panel and long-range polymerase chain reaction copy number variation analysis. Per the study protocol-defined negative medication effects in HD gene expansion carriers with a genotype predicted poor or ultrarapid metabolizer phenotype will be compared with HD gene expansion carriers with a predicted intermediate and normal metabolizer phenotype. Frequencies will be analyzed via χ2 and logistic multivariate regression analysis. In addition, we summarize in this manuscript HD-relevant PGx prescription recommendations to improve drug therapy. Ethics: The original study protocol was approved by the medical research ethics committee Leiden Den Haag Delft on 26 November 2019. Discussion: HD-MED is a low-risk study that will generate personalized PGx results that can immediately be implemented in clinical practice, thus potentially improving pharmacovigilance and patients' quality of life. Registration: This study is registered in the International Clinical Trial Registry Platform under registration number NL8251, URL https://trialsearch.who.int/Trial2.aspx?TrialID=NL8251.

Physiologically based pharmacokinetic modeling of tacrolimus for food-drug and CYP3A drug-drug-gene interaction predictions.

The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), high inter- and intra-individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug-drug interactions, acting as a victim drug when coadministered with CYP3A perpetrators. This work describes the development of a whole-body physiologically based pharmacokinetic model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food-drug interactions [FDIs]) and (ii) drug-drug(-gene) interactions (DD[G]Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK-Sim® Version 10 using a total of 37 whole blood concentration-time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate-release and extended-release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI area under the curve determined between first and last concentration measurements (AUClast ) and 6/6 predicted FDI maximum whole blood concentration (Cmax ) ratios within twofold of the respective observed ratios. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within twofold of their observed values. Potential applications of the final model include model-informed drug discovery and development or the support of model-informed precision dosing.

Physiologically-based pharmacokinetic modeling of quinidine to establish a CYP3A4, P-gp, and CYP2D6 drug-drug-gene interaction network.

The antiarrhythmic agent quinidine is a potent inhibitor of cytochrome P450 (CYP) 2D6 and P-glycoprotein (P-gp) and is therefore recommended for use in clinical drug-drug interaction (DDI) studies. However, as quinidine is also a substrate of CYP3A4 and P-gp, it is susceptible to DDIs involving these proteins. Physiologically-based pharmacokinetic (PBPK) modeling can help to mechanistically assess the absorption, distribution, metabolism, and excretion processes of a drug and has proven its usefulness in predicting even complex interaction scenarios. The objectives of the presented work were to develop a PBPK model of quinidine and to integrate the model into a comprehensive drug-drug(-gene) interaction (DD(G)I) network with a diverse set of CYP3A4 and P-gp perpetrators as well as CYP2D6 and P-gp victims. The quinidine parent-metabolite model including 3-hydroxyquinidine was developed using pharmacokinetic profiles from clinical studies after intravenous and oral administration covering a broad dosing range (0.1-600 mg). The model covers efflux transport via P-gp and metabolic transformation to either 3-hydroxyquinidine or unspecified metabolites via CYP3A4. The 3-hydroxyquinidine model includes further metabolism by CYP3A4 as well as an unspecific hepatic clearance. Model performance was assessed graphically and quantitatively with greater than 90% of predicted pharmacokinetic parameters within two-fold of corresponding observed values. The model was successfully used to simulate various DD(G)I scenarios with greater than 90% of predicted DD(G)I pharmacokinetic parameter ratios within two-fold prediction success limits. The presented network will be provided to the research community and can be extended to include further perpetrators, victims, and targets, to support investigations of DD(G)Is.

Why We Need to Take a Closer Look at Genetic Contributions to CYP3A Activity.

Cytochrome P450 3A (CYP3A) subfamily enzymes are involved in the metabolism of 40% of drugs in clinical use. Twin studies have indicated that 66% of the variability in CYP3A4 activity is hereditary. Yet, the complexity of the CYP3A locus and the lack of distinct drug metabolizer phenotypes has limited the identification and clinical application of CYP3A genetic variants compared to other Cytochrome P450 enzymes. In recent years evidence has emerged indicating that a substantial part of the missing heritability is caused by low frequency genetic variation. In this review, we outline the current pharmacogenomics knowledge of CYP3A activity and discuss potential future directions to improve our genetic knowledge and ability to explain CYP3A variability.

Substrate specificity of CYP2D6 genetic variants.

Genetic variation in the gene encoding CYP2D6 is used to guide drug prescribing in clinical practice. However, genetic variants in CYP2D6 show substrate-specific effects that are currently not accounted for. With a systematic literature, we retrieved 22 original studies describing in vitro experiments focusing on CYP2D6 alleles (CYP2D6*1, *2, *10 and *17) and substrates. Allele activity (clearance of the allele of interest divided by the clearance of the wildtype) was extracted. The results support the hypothesis of the existence of substrate specificity of the CYP2D6*17-allele (higher debrisoquine clearance), a subtle effect of the CYP2D6*10-allele (lower dextromethorphan clearance) but no substrate-specific effect of the CYP2D6*2-allele. Although our results support substrate specificity, for most substrates data are too sparse and require further studies.

Toward predicting CYP2D6-mediated variable drug response from CYP2D6 gene sequencing data.

Pharmacogenomics is a key component of personalized medicine that promises safer and more effective drug treatment by individualizing drug choice and dose based on genetic profiles. In clinical practice, genetic biomarkers are used to categorize patients into *-alleles to predict CYP450 enzyme activity and adjust drug dosages accordingly. However, this approach leaves a large part of variability in drug response unexplained. Here, we present a proof-of-concept approach that uses continuous-scale (instead of categorical) assignments to predict enzyme activity. We used full CYP2D6 gene sequences obtained with long-read amplicon-based sequencing and cytochrome P450 (CYP) 2D6-mediated tamoxifen metabolism data from a prospective study of 561 patients with breast cancer to train a neural network. The model explained 79% of interindividual variability in CYP2D6 activity compared to 54% with the conventional *-allele approach, assigned enzyme activities to known alleles with previously reported effects, and predicted the activity of previously uncharacterized combinations of variants. The results were replicated in an independent cohort of tamoxifen-treated patients (model R 2 adjusted = 0.66 versus *-allele R 2 adjusted = 0.35) and a cohort of patients treated with the CYP2D6 substrate venlafaxine (model R 2 adjusted = 0.64 versus *-allele R 2 adjusted = 0.55). Human embryonic kidney cells were used to confirm the effect of five genetic variants on metabolism of the CYP2D6 substrate bufuralol in vitro. These results demonstrate the advantage of a continuous scale and a completely phased genotype for prediction of CYP2D6 enzyme activity and could potentially enable more accurate prediction of individual drug response.

Technologies for Pharmacogenomics: A Review.

The continuous development of new genotyping technologies requires awareness of their potential advantages and limitations concerning utility for pharmacogenomics (PGx). In this review, we provide an overview of technologies that can be applied in PGx research and clinical practice. Most commonly used are single nucleotide variant (SNV) panels which contain a pre-selected panel of genetic variants. SNV panels offer a short turnaround time and straightforward interpretation, making them suitable for clinical practice. However, they are limited in their ability to assess rare and structural variants. Next-generation sequencing (NGS) and long-read sequencing are promising technologies for the field of PGx research. Both NGS and long-read sequencing often provide more data and more options with regard to deciphering structural and rare variants compared to SNV panels-in particular, in regard to the number of variants that can be identified, as well as the option for haplotype phasing. Nonetheless, while useful for research, not all sequencing data can be applied to clinical practice yet. Ultimately, selecting the right technology is not a matter of fact but a matter of choosing the right technique for the right problem.

Repurposing of Diagnostic Whole Exome Sequencing Data of 1,583 Individuals for Clinical Pharmacogenetics.

For ~ 80 drugs, widely recognized pharmacogenetics dosing guidelines are available. However, the use of these guidelines in clinical practice remains limited as only a fraction of patients is subjected to pharmacogenetic screening. We investigated the feasibility of repurposing whole exome sequencing (WES) data for a panel of 42 variants in 11 pharmacogenes to provide a pharmacogenomic profile. Existing diagnostic WES-data from child-parent trios totaling 1,583 individuals were used. Results were successfully extracted for 39 variants. No information could be extracted for three variants, located in CYP2C19, UGT1A1, and CYP3A5, and for CYP2D6 copy number. At least one actionable phenotype was present in 86% of the individuals. Haplotype phasing proved relevant for CYP2B6 assignments as 1.5% of the phenotypes were corrected after phasing. In conclusion, repurposing WES-data can yield meaningful pharmacogenetic profiles for 7 of 11 important pharmacogenes, which can be used to guide drug treatment.

Antibiotic Overuse in Children with Respiratory Syncytial Virus Lower Respiratory Tract Infection.

BACKGROUND: Respiratory syncytial virus (RSV) is the most common cause of lower respiratory tract infections (LRTI) during the first year of life. Antibiotic treatment is recommended in cases suspected of bacterial coinfection. The aim of this prospective study was to estimate the incidence of bacterial coinfections and the amount of antibiotic overuse in children infected with RSV using expert panel diagnosis. METHODS: Children 1 month of age and over with LRTI or fever without source were prospectively recruited in hospitals in the Netherlands and Israel. Children with confirmed RSV infection by Polymerase Chain Reaction (PCR) on nasal swabs were evaluated by an expert panel as reference standard diagnosis. Three experienced pediatricians distinguished bacterial coinfection from simple viral infection using all available clinical information, including all microbiologic evaluations and a 28-day follow-up evaluation. RESULTS: A total of 188 children (24% of all 784 recruited patients) were positive for RSV. From these, 92 (49%) were treated with antibiotics. All 27 children (29%) with bacterial coinfection were treated with antibiotics. Fifty-seven patients (62%) were treated with antibiotics without a diagnosis of bacterial coinfection. In 8 of the 92 (9%), the expert panel could not distinguish simple viral infection from bacterial coinfection. CONCLUSION: This is the first prospective international multicenter RSV study using an expert panel as reference standard to identify children with and without bacterial coinfection. All cases of bacterial coinfections are treated, whereas as many as one-third of all children with RSV LRTI are treated unnecessarily with antibiotics.